Economic Attitudes and Financial Decisions Among Welfare Recipients: Considerations for Workforce Policy
Bibliographic record
Abstract
This study investigates economic decision-making behaviors among welfare recipients in Miami, Florida, by leveraging well-established experimental protocols: the Guessing Game, the Prudence Measurement Task, the Risk Aversion Task, and the Stag Hunt Game. For this purpose, our study defines financial decisions as the underlying individual preferences that serve as validated proxies for savings behavior, debt management, job-search intensity, and participation in cooperative finance. A central objective is to compare the behavior of welfare recipients to that of undergraduate students, a cohort typically used in experimental economics research. The analysis reveals significant differences between the two groups in strategic thinking and coordination, particularly across ethnic and gender lines. Non-Hispanic/Latino participants in Miami displayed significantly higher average guesses in the Guessing Game compared to their counterparts in Tucson, indicating potential discrepancies in the depth of strategic reasoning. Additionally, female participants in Tucson exhibited higher levels of coordination in the Stag Hunt Game compared to females in Miami, suggesting variance in cooperative behavior between these groups. Despite these findings, regression models demonstrate that location, gender, and ethnicity collectively account for only a small fraction of the observed variance, as evidenced by low R2 values and substantial mean squared errors across all games. These results suggest that individual heterogeneity, rather than broad demographic variables, may be more influential in shaping economic decisions. This study underscores the complexity of generalizing findings from traditional student samples to more diverse populations, highlighting the need for further investigation into the socioeconomic factors that drive financial decision-making.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".